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--- |
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tags: |
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- bertopic |
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library_name: bertopic |
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pipeline_tag: text-classification |
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--- |
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# BERTopic_Political |
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. |
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BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. |
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## Usage |
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To use this model, please install BERTopic: |
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``` |
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pip install -U bertopic |
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``` |
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You can use the model as follows: |
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```python |
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from bertopic import BERTopic |
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topic_model = BERTopic.load("karinegabsschon/BERTopic_Political") |
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topic_model.get_topic_info() |
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``` |
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## Topic overview |
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* Number of topics: 20 |
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* Number of training documents: 619 |
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<details> |
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<summary>Click here for an overview of all topics.</summary> |
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| Topic ID | Topic Keywords | Topic Frequency | Label | |
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|----------|----------------|-----------------|-------| |
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| -1 | electric - tariffs - vehicles - ev - car | 11 | -1_electric_tariffs_vehicles_ev | |
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| 0 | cars - spd - tax - electric - purchase | 97 | 0_cars_spd_tax_electric | |
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| 1 | charging - chargers - public - ev - points | 87 | 1_charging_chargers_public_ev | |
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| 2 | tax - car - new - electric - petrol | 72 | 2_tax_car_new_electric | |
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| 3 | tesla - musk - elon - elon musk - trump | 53 | 3_tesla_musk_elon_elon musk | |
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| 4 | moves - aid - electric - euros - plan | 49 | 4_moves_aid_electric_euros | |
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| 5 | byd - chinese - china - price - price war | 36 | 5_byd_chinese_china_price | |
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| 6 | targets - government - mandate - starmer - zero | 25 | 6_targets_government_mandate_starmer | |
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| 7 | euros - bonus - ecological - ecological bonus - electric | 21 | 7_euros_bonus_ecological_ecological bonus | |
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| 8 | california - trump - states - administration - electric | 21 | 8_california_trump_states_administration | |
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| 9 | tariffs - united states - united - states - plant | 20 | 9_tariffs_united states_united_states | |
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| 10 | ukraine - region - electric - ukrainian - vehicles | 18 | 10_ukraine_region_electric_ukrainian | |
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| 11 | tesla - city - toronto - canadian - chow | 16 | 11_tesla_city_toronto_canadian | |
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| 12 | eu - china - chinese - tariffs - minimum | 15 | 12_eu_china_chinese_tariffs | |
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| 13 | chinese - defence - security - spying - military | 15 | 13_chinese_defence_security_spying | |
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| 14 | european - eu - commission - industry - electric | 14 | 14_european_eu_commission_industry | |
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| 15 | huf - businesses - subsidies - hungary - battery | 13 | 15_huf_businesses_subsidies_hungary | |
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| 16 | cent - government - diesel - fleet - electric | 12 | 16_cent_government_diesel_fleet | |
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| 17 | credit - tax - electric - vehicles - electric vehicles | 12 | 17_credit_tax_electric_vehicles | |
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| 18 | british - trade - cars - government - tariffs | 12 | 18_british_trade_cars_government | |
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</details> |
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## Training hyperparameters |
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* calculate_probabilities: False |
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* language: None |
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* low_memory: False |
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* min_topic_size: 10 |
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* n_gram_range: (1, 1) |
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* nr_topics: None |
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* seed_topic_list: None |
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* top_n_words: 10 |
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* verbose: True |
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* zeroshot_min_similarity: 0.7 |
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* zeroshot_topic_list: None |
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## Framework versions |
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* Numpy: 2.0.2 |
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* HDBSCAN: 0.8.40 |
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* UMAP: 0.5.8 |
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* Pandas: 2.2.2 |
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* Scikit-Learn: 1.6.1 |
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* Sentence-transformers: 4.1.0 |
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* Transformers: 4.53.0 |
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* Numba: 0.60.0 |
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* Plotly: 5.24.1 |
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* Python: 3.11.13 |
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